import joblib import pandas as pd from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.compose import make_column_transformer from sklearn.pipeline import make_pipeline from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error, r2_score data_df = pd.read_csv("insurance.csv") target = 'charges' numeric_features = ['age', 'bmi', 'children'] categorical_features = ['sex', 'smoker', 'region'] print("Creating data subsets") X = data_df[numeric_features + categorical_features] y = data_df[target] Xtrain, Xtest, ytrain, ytest = train_test_split( X, y, test_size=0.2, random_state=42 ) preprocessor = make_column_transformer( (StandardScaler(), numeric_features), (OneHotEncoder(handle_unknown='ignore'), categorical_features) ) model_linear_regression = LinearRegression(n_jobs=-1) print("Estimating Model Pipeline") model_pipeline = make_pipeline( preprocessor, model_linear_regression ) model_pipeline.fit(Xtrain, ytrain) print("Logging Metrics") print(f"R-squared: {r2_score(ytest, model_pipeline.predict(Xtest))}") print("Serializing Model") saved_model_path = "model.joblib" joblib.dump(model_pipeline, saved_model_path)